Advertisement

Dynamic Pricing Algorithms for Task Allocation in Multi-agent Swarms

  • Prithviraj Dasgupta
  • Matthew Hoeing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5043)

Abstract

Over the past few years, emergent computing based techniques such as swarming have evolved as an attractive technique to design coordination protocols in large-scale distributed systems and massively multi-agent systems. In this paper, we consider a search-and-execute problem domain where agents have to discover tasks online and perform them in a distributed, collaborative manner. We specifically focus on the problem of distributed coordination between agents to dynamically allocate the tasks among themselves. To address this problem, we describe a novel technique that combines a market-based dynamic pricing algorithm to control the task priorities with a swarming-based coordination technique to disseminate task information across the agents. Experimental results within a simulated environment for a distributed aided target recognition application show that the dynamic pricing based task selection strategies compare favorably with other heuristic-based task selection strategies in terms of task completion times while achieving a significant reduction in communication overhead.

Keywords

Communication Overhead Dynamic Price Task Allocation Task Selection Task Completion Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abraham, A., Grosan, C., Ramos, V. (eds.): Swarm Intelligence in Data Mining, Studies in Computational Intelligence, vol. 34. Springer, Heidelberg (2006)Google Scholar
  2. 2.
    Alami, R., Fleury, S., Herrb, M., Ingrand, F., Robert, F.: Multi-robot cooperation in the MARTHA project. IEEE Robotics and Automation 5(1), 36–47 (1998)CrossRefGoogle Scholar
  3. 3.
    Babaoglu, O., Meling, H., Montresor, A.: Anthill: A Framework for the Development of Agent-Based Peer-to-Peer Systems. In: Proc. Intl. Conf. on Distributed Computing Systems, pp. 15–22 (2002)Google Scholar
  4. 4.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)zbMATHGoogle Scholar
  5. 5.
    Bullnheimer, B., Hartl, R., Strauss, C.: An improved Ant system algorithm for the vehicle routing problem. In: POM working paper no. 10/97, Vienna (1997)Google Scholar
  6. 6.
    Clark, J., Fierro, R.: Cooperative hybrid control of robotic sensors for perimeter detection and tracking. In: Proc. American Control Conference, vol. 5, pp. 3500–3505 (2005)Google Scholar
  7. 7.
    Correll, N., Martinoli, A.: Distributed Coverage: From Deterministic to Probabilistic Model. In: Proc. of the 2007 IEEE Int. Conf. on Robotics and Automation, Rome, Italy, April 2007, pp. 379–384 (2007)Google Scholar
  8. 8.
    Dasgupta, P., Das, R.: Dynamic Pricing with Limited Competitor Information in a Multi-Agent Economy. In: Proc. 7th Intl. Conf. on Cooperative Information Systems, Eilat, Israel, pp. 299–310 (2000)Google Scholar
  9. 9.
    Dasgupta, P., Hashimoto, Y.: Multi-Attribute Dynamic Pricing for Online Markets Using Intelligent Agents. In: Proc. AAMAS 2004, pp. 277–284 (2004)Google Scholar
  10. 10.
    Dasgupta, P., O’Hara, S., Petrov, P.: A Multi-agent UAV Swarm for Automatic Target Recognition. In: Thompson, S.G., Ghanea-Hercock, R. (eds.) DAMAS 2005. LNCS (LNAI), vol. 3890, pp. 80–91. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Dasgupta, P.: Distributed automatic target recognition using multi-agent UAV swarms. In: Proc. AAMAS 2006, pp. 479–481 (2006)Google Scholar
  12. 12.
    Dasgupta, P., Hoeing, M.: Market based Distributed Task Selection in Multi-agent Swarms. In: Proc. Intl. Conf. on Intelligent Agent Technology (IAT 2006), Hong Kong, pp. 113–116 (2006)Google Scholar
  13. 13.
    Dias, M.B., Zlot, R.M., Kalra, N., Stentz, A.: Market-Based Multirobot Coordination: A Survey and Analysis, Tech. report CMU-RI-TR-05-13, Robotics Institute, Carnegie Mellon University (2005)Google Scholar
  14. 14.
    Di Caro, G., Ducatelle, F., Gambardella, L.: AntHocNet: An Ant-Based Hybrid Routing Algorithm for Mobile Ad Hoc Networks. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 461–470. Springer, Heidelberg (2004)Google Scholar
  15. 15.
    Edwards, S.: Swarming on the Battlefield: Past, present and future. In: RAND National Security Research Division Report (2000)Google Scholar
  16. 16.
    Gaudiano, F., Bonabeau, E., Shargel, B.: Evolving behaviors for a swarm of unmanned air vehicles. In: Proc. of the 2005 IEEE Swarm Intelligence Symposium, Pasadena, CA, pp. 317–324 (2005)Google Scholar
  17. 17.
    Gerkey, B.: On multi-robot task allocation, Ph.D Thesis, Univ. of Southern California (2003)Google Scholar
  18. 18.
    Jager, M., Nebel, B.: Dynamic Decentralized Area Partitioning for Cooperating Cleaning Robots. In: Proceedings of the 2002 IEEE Intl. Conf. on Robotics and Automation, Washington, DC, USA, pp. 3577–3582 (2002)Google Scholar
  19. 19.
    Kalra, N., Martinoli, A.: A Comparative Study of Market-Based and Threshold-Based Task Allocation. In: Proc. 8th Intl. Symp. on Distributed Autonomous Robotic Systems (DARS 2006) (2006)Google Scholar
  20. 20.
    Kephart, J., Greenwald, A.: Shopbot Economics. Autonomous Agents and Multi-Agent Systems 5(3), 255–287 (2002)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Lemaire, T., Alami, R., Lacroix, S.: A distributed tasks allocation scheme in multi-uav context. In: IEEE Intl. Conf. on Robotics and Automation, New Orleans, LA (USA), April 2004, pp. 3622–3627 (2004)Google Scholar
  22. 22.
    Mailler, R., Lesser, V., Horling, B.: Cooperative negotiation for soft real-time distributed resource allocation. In: Proc. AAMAS 2003, pp. 576–583 (2003)Google Scholar
  23. 23.
    Mailler, R., Lesser, V.: A cooperative mediation based protocol for dynamic, distributed resource allocation. IEEE Trans. on System, Man, Cybernetics, Part C 36(1), 80–91 (2006)CrossRefGoogle Scholar
  24. 24.
    Mainland, G., Parkes, D., Welsh, M.: Decentralized, Adaptive Resource Allocation for Sensor Networks. In: Proc. 2nd Symp. on Networked Systems Design and Implementation(NSDI 2005) (2005), http://www.usenix.org/events/nsdi05/tech
  25. 25.
    Miller, D., Dasgupta, P., Judkins, T.: Distributed Task Selection in Multi-agent based Swarms using Heuristic Strategies. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) SAB 2006 Ws 2007. LNCS, vol. 4433, pp. 158–172. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  26. 26.
    Modi, P., Shen, W., Tambe, M., Yokoo, M.: Adopt: asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligence 161(1-2), 149–180 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Oritz, C., Vincent, R., Morriset, B.: Task Inference and Distributed Task Management in the Centibots Robotic System. In: AAMAS 2004, pp. 870–877 (2005)Google Scholar
  28. 28.
    Parker, L.: Distributed Algorithms for Multi-Robot Observation of Multiple Moving Targets. Autonomous Robots 12(3), 231–255 (2002)zbMATHCrossRefGoogle Scholar
  29. 29.
    Sauter, J., Matthews, R., Parunak, H.V.D., Brueckner, S.: Evolving Adaptive Pheromone Path Planning Mechanisms. In: AAMAS 2000 and AAMAS 2002, pp. 434–440 (2002)Google Scholar
  30. 30.
    Sauter, J., Matthews, R., Parunak, H., Brueckner, S.: Performance of Digital Pheromones for Swarming Vehicle Control. In: Proc. AAMAS 2005, Utrecht, The Netherlands, pp. 903–910 (2005)Google Scholar
  31. 31.
    Shehory, O., Kraus, S.: Methods for task allocation via agent coalition formation. Artificial Intelligence 101(1-2), 165–200 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  32. 32.
    Smith, R.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers 29, 1104–1113 (1980)CrossRefGoogle Scholar
  33. 33.
    Parunak, H., Brueckner, S., Odell, J.: Swarming coordination of multiple UAVs for collaborative sensing. In: Proc. 2nd AIAA Unmanned Unlimited Systems, Technologies, and Operations Aerospace Land and Sea Conference Workshop and Exhibits, San Diego, CA (2003), http://www.newvectors.net/staff/parunakv/AIAA03.pdf
  34. 34.
    Weiss, G. (ed.): Multi Agent Systems. MIT Press, Cambridge (1998)Google Scholar
  35. 35.
    Werfel, J., Bar-Yam, Y., Nagpal, R.: Building Patterned Structures with Robot Swarms. In: IJCAI 2005, pp. 1495–1504 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Prithviraj Dasgupta
    • 1
  • Matthew Hoeing
    • 1
  1. 1.Computer Science DepartmentUniversity of NebraskaOmaha

Personalised recommendations